Lead LLM Efficiency Research

About the Role

As a Lead LLM Efficiency Researcher, you will drive the investigation and implementation of novel technologies to enhance the efficiency of Large Language Models. This involves exploring new architectures, refining pre-training methods, and integrating LLMs with other AI models, such as computer vision models.

• Lead the research of technology for improving the efficiency of Large Language Model (LLM) while performing target capabilities or supporting many capabilities, such as novel architectures and improved pre-training.
• Design and implement NLP algorithms for model training and prediction, leverage ML infrastructure, and contribute to model optimization and data processing, using Pytorch or other frameworks.
• Integrate and improve LLM algorithms to work with other models such as computer vision models.
• Identify defined problems/gaps in existing technology and engage other Research teams, stakeholders and leaders to expand efficient LLM technology.
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies.
• Write up results in design documents, technical reports, and papers for publication.
• Represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.
• Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.